Deep Principle, a Hangzhou startup, has released Hydrogen-1.0, a 753-billion-parameter model post-trained for chemistry, materials science, and life sciences. The company publishes the weights on Hugging Face under the MIT license, giving research groups a path to inspect, adapt, and run the model on their own infrastructure.

Co-founder and CTO Chenru Duan describes Hydrogen-1.0 as the company’s first open-weight research model. It starts from Z.ai’s GLM-5.3 and adds training on internal scientific data, laboratory records, and discovery trajectories. Deep Principle reports a 38.3% pass rate on OpenAI’s FrontierScience Research evaluation, compared with 33.3% for GPT-6 Astra. Those results remain vendor-reported.

What 753 billion parameters require

Hydrogen-1.0 uses a mixture-of-experts architecture, which routes each token through a subset of specialized blocks instead of activating every parameter. Its glm_moe_dsa architecture tag indicates that it inherits GLM-5.3’s mixture-of-experts design and deep sparse attention. Deep Principle has not disclosed the number of active parameters per token, an important figure for estimating inference speed and compute requirements.

The advertised context window is one million tokens, with a maximum output of 128,000 tokens. Long contexts also expand the key-value cache used during inference, so practical memory requirements extend beyond the model weights.

The estimates cover parameter storage alone. Quantization scales, runtime buffers, the context cache, and framework overhead increase actual memory use. The Hugging Face repository is gated, so users must sign in and accept its terms before downloading the model shards.

Lab trajectories shape the training

Deep Principle trained Hydrogen-1.0 on complete experimental trajectories that include question framing, procedure design, observations, failed attempts, and subsequent revisions. That material can teach relationships between decisions and outcomes that finished papers often omit.